Overview

Dataset statistics

Number of variables29
Number of observations14266
Missing cells2705
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 MiB
Average record size in memory232.0 B

Variable types

Categorical13
Numeric16

Alerts

C OF O STATUS has constant value "CO Issued" Constant
HOUSE NO has a high cardinality: 3741 distinct values High cardinality
STREET NAME has a high cardinality: 3301 distinct values High cardinality
SUBMITTED DATE has a high cardinality: 399 distinct values High cardinality
C OF O ISSUANCE DATE has a high cardinality: 14258 distinct values High cardinality
APPLICATION NUMBER has a high cardinality: 14265 distinct values High cardinality
nta has a high cardinality: 193 distinct values High cardinality
ntaName has a high cardinality: 193 distinct values High cardinality
BIN is highly correlated with ZIP CODE and 6 other fieldsHigh correlation
BLOCK is highly correlated with ZIP CODE and 7 other fieldsHigh correlation
ZIP CODE is highly correlated with BIN and 9 other fieldsHigh correlation
COMMUNITY BOARD is highly correlated with BIN and 7 other fieldsHigh correlation
xCoordinate is highly correlated with BLOCK and 2 other fieldsHigh correlation
yCoordinate is highly correlated with latitude and 1 other fieldsHigh correlation
latitude is highly correlated with yCoordinate and 1 other fieldsHigh correlation
longitude is highly correlated with BLOCK and 2 other fieldsHigh correlation
communityDistrict is highly correlated with BIN and 7 other fieldsHigh correlation
communityDistrictBoroughCode is highly correlated with BIN and 6 other fieldsHigh correlation
communityDistrictNumber is highly correlated with BLOCKHigh correlation
cityCouncilDistrict is highly correlated with BIN and 8 other fieldsHigh correlation
censusTract2010 is highly correlated with BLOCK and 1 other fieldsHigh correlation
buildingIdentificationNumber is highly correlated with BIN and 6 other fieldsHigh correlation
bbl is highly correlated with BIN and 8 other fieldsHigh correlation
BIN is highly correlated with ZIP CODE and 8 other fieldsHigh correlation
BLOCK is highly correlated with communityDistrictNumberHigh correlation
ZIP CODE is highly correlated with BIN and 8 other fieldsHigh correlation
COMMUNITY BOARD is highly correlated with BIN and 8 other fieldsHigh correlation
xCoordinate is highly correlated with ZIP CODE and 1 other fieldsHigh correlation
yCoordinate is highly correlated with BIN and 7 other fieldsHigh correlation
latitude is highly correlated with BIN and 7 other fieldsHigh correlation
longitude is highly correlated with ZIP CODE and 1 other fieldsHigh correlation
communityDistrict is highly correlated with BIN and 8 other fieldsHigh correlation
communityDistrictBoroughCode is highly correlated with BIN and 8 other fieldsHigh correlation
communityDistrictNumber is highly correlated with BLOCKHigh correlation
cityCouncilDistrict is highly correlated with BIN and 8 other fieldsHigh correlation
buildingIdentificationNumber is highly correlated with BIN and 8 other fieldsHigh correlation
bbl is highly correlated with BIN and 8 other fieldsHigh correlation
BIN is highly correlated with ZIP CODE and 6 other fieldsHigh correlation
BLOCK is highly correlated with censusTract2010 and 1 other fieldsHigh correlation
ZIP CODE is highly correlated with BIN and 5 other fieldsHigh correlation
COMMUNITY BOARD is highly correlated with BIN and 6 other fieldsHigh correlation
xCoordinate is highly correlated with longitudeHigh correlation
yCoordinate is highly correlated with latitude and 1 other fieldsHigh correlation
latitude is highly correlated with yCoordinate and 1 other fieldsHigh correlation
longitude is highly correlated with xCoordinateHigh correlation
communityDistrict is highly correlated with BIN and 6 other fieldsHigh correlation
communityDistrictBoroughCode is highly correlated with BIN and 6 other fieldsHigh correlation
cityCouncilDistrict is highly correlated with BIN and 6 other fieldsHigh correlation
censusTract2010 is highly correlated with BLOCKHigh correlation
buildingIdentificationNumber is highly correlated with BIN and 5 other fieldsHigh correlation
bbl is highly correlated with BIN and 7 other fieldsHigh correlation
C OF O STATUS is highly correlated with communityDistrictBoroughCode and 4 other fieldsHigh correlation
communityDistrictBoroughCode is highly correlated with C OF O STATUS and 1 other fieldsHigh correlation
BOROUGH is highly correlated with C OF O STATUS and 1 other fieldsHigh correlation
JOB TYPE is highly correlated with C OF O STATUS and 1 other fieldsHigh correlation
JOB FILING NAME is highly correlated with C OF O STATUS and 1 other fieldsHigh correlation
C OF O FILING TYPE is highly correlated with C OF O STATUSHigh correlation
JOB FILING NAME is highly correlated with JOB TYPEHigh correlation
JOB TYPE is highly correlated with JOB FILING NAME and 1 other fieldsHigh correlation
BIN is highly correlated with JOB TYPE and 13 other fieldsHigh correlation
BOROUGH is highly correlated with BIN and 12 other fieldsHigh correlation
BLOCK is highly correlated with ZIP CODE and 4 other fieldsHigh correlation
ZIP CODE is highly correlated with BIN and 14 other fieldsHigh correlation
C OF O FILING TYPE is highly correlated with ZIP CODEHigh correlation
COMMUNITY BOARD is highly correlated with BIN and 14 other fieldsHigh correlation
xCoordinate is highly correlated with BIN and 14 other fieldsHigh correlation
yCoordinate is highly correlated with BIN and 13 other fieldsHigh correlation
latitude is highly correlated with BIN and 13 other fieldsHigh correlation
longitude is highly correlated with BIN and 14 other fieldsHigh correlation
communityDistrict is highly correlated with BIN and 12 other fieldsHigh correlation
communityDistrictBoroughCode is highly correlated with BIN and 12 other fieldsHigh correlation
communityDistrictNumber is highly correlated with COMMUNITY BOARD and 7 other fieldsHigh correlation
cityCouncilDistrict is highly correlated with BIN and 13 other fieldsHigh correlation
censusTract2010 is highly correlated with BIN and 10 other fieldsHigh correlation
buildingIdentificationNumber is highly correlated with BIN and 11 other fieldsHigh correlation
bbl is highly correlated with BIN and 14 other fieldsHigh correlation
buildingIdentificationNumber has 672 (4.7%) missing values Missing
bbl has 672 (4.7%) missing values Missing
C OF O ISSUANCE DATE is uniformly distributed Uniform
APPLICATION NUMBER is uniformly distributed Uniform

Reproduction

Analysis started2022-06-30 20:49:57.606245
Analysis finished2022-06-30 20:50:48.872980
Duration51.27 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

JOB FILING NAME
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size111.6 KiB
01
14110 
I1
 
155
02
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters28532
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row01
2nd row01
3rd row01
4th row01
5th row01

Common Values

ValueCountFrequency (%)
0114110
98.9%
I1155
 
1.1%
021
 
< 0.1%

Length

2022-06-30T20:50:49.015678image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-30T20:50:49.282010image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
0114110
98.9%
i1155
 
1.1%
021
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
114265
50.0%
014111
49.5%
I155
 
0.5%
21
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28377
99.5%
Uppercase Letter155
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
114265
50.3%
014111
49.7%
21
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
I155
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common28377
99.5%
Latin155
 
0.5%

Most frequent character per script

Common
ValueCountFrequency (%)
114265
50.3%
014111
49.7%
21
 
< 0.1%
Latin
ValueCountFrequency (%)
I155
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII28532
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
114265
50.0%
014111
49.5%
I155
 
0.5%
21
 
< 0.1%

JOB TYPE
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size111.6 KiB
ALTERATION TYPE 1
7843 
NEW BUILDING
6268 
Alteration CO
 
111
New Building
 
32
CO - New Building with Existing Elements to Remain
 
12

Length

Max length50
Median length17
Mean length14.78858825
Min length12

Characters and Unicode

Total characters210974
Distinct characters35
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowALTERATION TYPE 1
2nd rowALTERATION TYPE 1
3rd rowALTERATION TYPE 1
4th rowALTERATION TYPE 1
5th rowALTERATION TYPE 1

Common Values

ValueCountFrequency (%)
ALTERATION TYPE 17843
55.0%
NEW BUILDING6268
43.9%
Alteration CO111
 
0.8%
New Building32
 
0.2%
CO - New Building with Existing Elements to Remain12
 
0.1%

Length

2022-06-30T20:50:49.402494image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-30T20:50:49.664669image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
alteration7954
21.8%
type7843
21.5%
17843
21.5%
new6312
17.3%
building6312
17.3%
co123
 
0.3%
12
 
< 0.1%
with12
 
< 0.1%
existing12
 
< 0.1%
elements12
 
< 0.1%
Other values (2)24
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T23529
11.2%
22193
10.5%
E21978
10.4%
N20423
9.7%
I20379
9.7%
A15797
 
7.5%
L14111
 
6.7%
O7966
 
3.8%
R7855
 
3.7%
17843
 
3.7%
Other values (25)48900
23.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter179231
85.0%
Space Separator22193
 
10.5%
Decimal Number7843
 
3.7%
Lowercase Letter1695
 
0.8%
Dash Punctuation12
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T23529
13.1%
E21978
12.3%
N20423
11.4%
I20379
11.4%
A15797
8.8%
L14111
7.9%
O7966
 
4.4%
R7855
 
4.4%
P7843
 
4.4%
Y7843
 
4.4%
Other values (6)31507
17.6%
Lowercase Letter
ValueCountFrequency (%)
t270
15.9%
i247
14.6%
e191
11.3%
n191
11.3%
l167
9.9%
a123
7.3%
o123
7.3%
r111
6.5%
w56
 
3.3%
g56
 
3.3%
Other values (6)160
9.4%
Space Separator
ValueCountFrequency (%)
22193
100.0%
Decimal Number
ValueCountFrequency (%)
17843
100.0%
Dash Punctuation
ValueCountFrequency (%)
-12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin180926
85.8%
Common30048
 
14.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
T23529
13.0%
E21978
12.1%
N20423
11.3%
I20379
11.3%
A15797
8.7%
L14111
7.8%
O7966
 
4.4%
R7855
 
4.3%
P7843
 
4.3%
Y7843
 
4.3%
Other values (22)33202
18.4%
Common
ValueCountFrequency (%)
22193
73.9%
17843
 
26.1%
-12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII210974
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T23529
11.2%
22193
10.5%
E21978
10.4%
N20423
9.7%
I20379
9.7%
A15797
 
7.5%
L14111
 
6.7%
O7966
 
3.8%
R7855
 
3.7%
17843
 
3.7%
Other values (25)48900
23.2%

BIN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7513
Distinct (%)52.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2814861.658
Minimum1000003
Maximum5863352
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size111.6 KiB
2022-06-30T20:50:49.815181image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1000003
5-th percentile1009431.5
Q11088634.75
median3072409
Q34051863.5
95-th percentile5068897
Maximum5863352
Range4863349
Interquartile range (IQR)2963228.75

Descriptive statistics

Standard deviation1410278.23
Coefficient of variation (CV)0.5010115599
Kurtosis-1.343764029
Mean2814861.658
Median Absolute Deviation (MAD)1207568.5
Skewness-0.0166120269
Sum4.015681641 × 1010
Variance1.988884686 × 1012
MonotonicityNot monotonic
2022-06-30T20:50:50.114537image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
333588414
 
0.1%
300008813
 
0.1%
586316512
 
0.1%
300041711
 
0.1%
300017110
 
0.1%
318554310
 
0.1%
108082410
 
0.1%
40035409
 
0.1%
33969629
 
0.1%
21292248
 
0.1%
Other values (7503)14160
99.3%
ValueCountFrequency (%)
10000035
< 0.1%
10000052
 
< 0.1%
10000372
 
< 0.1%
10000454
< 0.1%
10000572
 
< 0.1%
10000581
 
< 0.1%
10000606
< 0.1%
10008094
< 0.1%
10008117
< 0.1%
10008133
< 0.1%
ValueCountFrequency (%)
58633521
 
< 0.1%
586316512
0.1%
58206491
 
< 0.1%
58172091
 
< 0.1%
58165981
 
< 0.1%
58148261
 
< 0.1%
58103251
 
< 0.1%
51755581
 
< 0.1%
51754631
 
< 0.1%
51753521
 
< 0.1%

BOROUGH
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size111.6 KiB
MANHATTAN
4548 
BROOKLYN
4529 
QUEENS
3045 
BRONX
1167 
STATEN ISLAND
977 

Length

Max length13
Median length9
Mean length7.988924716
Min length5

Characters and Unicode

Total characters113970
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMANHATTAN
2nd rowMANHATTAN
3rd rowMANHATTAN
4th rowMANHATTAN
5th rowMANHATTAN

Common Values

ValueCountFrequency (%)
MANHATTAN4548
31.9%
BROOKLYN4529
31.7%
QUEENS3045
21.3%
BRONX1167
 
8.2%
STATEN ISLAND977
 
6.8%

Length

2022-06-30T20:50:50.395815image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-30T20:50:50.649000image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
manhattan4548
29.8%
brooklyn4529
29.7%
queens3045
20.0%
bronx1167
 
7.7%
staten977
 
6.4%
island977
 
6.4%

Most occurring characters

ValueCountFrequency (%)
N19791
17.4%
A15598
13.7%
T11050
9.7%
O10225
9.0%
E7067
 
6.2%
B5696
 
5.0%
R5696
 
5.0%
L5506
 
4.8%
S4999
 
4.4%
M4548
 
4.0%
Other values (9)23794
20.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter112993
99.1%
Space Separator977
 
0.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N19791
17.5%
A15598
13.8%
T11050
9.8%
O10225
9.0%
E7067
 
6.3%
B5696
 
5.0%
R5696
 
5.0%
L5506
 
4.9%
S4999
 
4.4%
M4548
 
4.0%
Other values (8)22817
20.2%
Space Separator
ValueCountFrequency (%)
977
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin112993
99.1%
Common977
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
N19791
17.5%
A15598
13.8%
T11050
9.8%
O10225
9.0%
E7067
 
6.3%
B5696
 
5.0%
R5696
 
5.0%
L5506
 
4.9%
S4999
 
4.4%
M4548
 
4.0%
Other values (8)22817
20.2%
Common
ValueCountFrequency (%)
977
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII113970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N19791
17.4%
A15598
13.7%
T11050
9.7%
O10225
9.0%
E7067
 
6.2%
B5696
 
5.0%
R5696
 
5.0%
L5506
 
4.8%
S4999
 
4.4%
M4548
 
4.0%
Other values (9)23794
20.9%

HOUSE NO
Categorical

HIGH CARDINALITY

Distinct3741
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Memory size111.6 KiB
1
 
125
11
 
76
20
 
76
200
 
64
100
 
63
Other values (3736)
13862 

Length

Max length12
Median length11
Mean length3.523482406
Min length1

Characters and Unicode

Total characters50266
Distinct characters27
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1801 ?
Unique (%)12.6%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
1125
 
0.9%
1176
 
0.5%
2076
 
0.5%
20064
 
0.4%
10063
 
0.4%
2562
 
0.4%
4058
 
0.4%
5557
 
0.4%
4556
 
0.4%
1055
 
0.4%
Other values (3731)13574
95.1%

Length

2022-06-30T20:50:50.783675image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1125
 
0.9%
gar104
 
0.7%
2077
 
0.5%
1176
 
0.5%
20064
 
0.4%
10063
 
0.4%
2563
 
0.4%
5559
 
0.4%
4058
 
0.4%
4556
 
0.4%
Other values (3613)13684
94.8%

Most occurring characters

ValueCountFrequency (%)
19163
18.2%
26046
12.0%
05326
10.6%
54993
9.9%
34755
9.5%
44212
8.4%
63277
 
6.5%
73001
 
6.0%
82891
 
5.8%
-2829
 
5.6%
Other values (17)3773
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number46295
92.1%
Dash Punctuation2829
 
5.6%
Uppercase Letter968
 
1.9%
Space Separator163
 
0.3%
Other Punctuation6
 
< 0.1%
Lowercase Letter5
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
19163
19.8%
26046
13.1%
05326
11.5%
54993
10.8%
34755
10.3%
44212
9.1%
63277
 
7.1%
73001
 
6.5%
82891
 
6.2%
92631
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
A393
40.6%
G274
28.3%
R237
24.5%
E41
 
4.2%
B13
 
1.3%
X3
 
0.3%
C3
 
0.3%
P2
 
0.2%
I1
 
0.1%
D1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
a2
40.0%
r1
20.0%
g1
20.0%
e1
20.0%
Dash Punctuation
ValueCountFrequency (%)
-2829
100.0%
Space Separator
ValueCountFrequency (%)
163
100.0%
Other Punctuation
ValueCountFrequency (%)
/6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common49293
98.1%
Latin973
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A393
40.4%
G274
28.2%
R237
24.4%
E41
 
4.2%
B13
 
1.3%
X3
 
0.3%
C3
 
0.3%
P2
 
0.2%
a2
 
0.2%
I1
 
0.1%
Other values (4)4
 
0.4%
Common
ValueCountFrequency (%)
19163
18.6%
26046
12.3%
05326
10.8%
54993
10.1%
34755
9.6%
44212
8.5%
63277
 
6.6%
73001
 
6.1%
82891
 
5.9%
-2829
 
5.7%
Other values (3)2800
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII50266
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19163
18.2%
26046
12.0%
05326
10.6%
54993
9.9%
34755
9.5%
44212
8.4%
63277
 
6.5%
73001
 
6.0%
82891
 
5.8%
-2829
 
5.6%
Other values (17)3773
7.5%

STREET NAME
Categorical

HIGH CARDINALITY

Distinct3301
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Memory size111.6 KiB
BROADWAY
 
313
MADISON AVENUE
 
106
PARK AVENUE
 
81
FIFTH AVENUE
 
63
FLATBUSH AVENUE
 
62
Other values (3296)
13641 

Length

Max length29
Median length23
Mean length12.9249264
Min length3

Characters and Unicode

Total characters184387
Distinct characters47
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1171 ?
Unique (%)8.2%

Sample

1st rowSOUTH STREET
2nd rowSOUTH STREET
3rd rowSOUTH STREET
4th rowSOUTH STREET
5th rowSOUTH STREET

Common Values

ValueCountFrequency (%)
BROADWAY313
 
2.2%
MADISON AVENUE106
 
0.7%
PARK AVENUE81
 
0.6%
FIFTH AVENUE63
 
0.4%
FLATBUSH AVENUE62
 
0.4%
5TH AVENUE57
 
0.4%
SCHROEDERS AVENUE55
 
0.4%
BEDFORD AVENUE50
 
0.4%
FULTON STREET48
 
0.3%
HUDSON STREET46
 
0.3%
Other values (3291)13385
93.8%

Length

2022-06-30T20:50:51.029712image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
street6175
 
19.5%
avenue4050
 
12.8%
west1340
 
4.2%
east1133
 
3.6%
ave924
 
2.9%
st611
 
1.9%
road449
 
1.4%
place390
 
1.2%
broadway344
 
1.1%
blvd270
 
0.9%
Other values (1765)16036
50.6%

Most occurring characters

ValueCountFrequency (%)
E31435
17.0%
T21508
11.7%
18319
9.9%
A13409
 
7.3%
R13127
 
7.1%
S13084
 
7.1%
N9934
 
5.4%
V6310
 
3.4%
U5958
 
3.2%
O5897
 
3.2%
Other values (37)45406
24.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter154682
83.9%
Space Separator18319
 
9.9%
Decimal Number11170
 
6.1%
Other Punctuation202
 
0.1%
Lowercase Letter14
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E31435
20.3%
T21508
13.9%
A13409
8.7%
R13127
8.5%
S13084
8.5%
N9934
 
6.4%
V6310
 
4.1%
U5958
 
3.9%
O5897
 
3.8%
H4351
 
2.8%
Other values (16)29669
19.2%
Decimal Number
ValueCountFrequency (%)
12150
19.2%
21584
14.2%
31214
10.9%
51212
10.9%
41177
10.5%
6936
8.4%
7854
 
7.6%
8786
 
7.0%
9694
 
6.2%
0563
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
t4
28.6%
n3
21.4%
e2
14.3%
u1
 
7.1%
i1
 
7.1%
g1
 
7.1%
o1
 
7.1%
r1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
.184
91.1%
'18
 
8.9%
Space Separator
ValueCountFrequency (%)
18319
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin154696
83.9%
Common29691
 
16.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E31435
20.3%
T21508
13.9%
A13409
8.7%
R13127
8.5%
S13084
8.5%
N9934
 
6.4%
V6310
 
4.1%
U5958
 
3.9%
O5897
 
3.8%
H4351
 
2.8%
Other values (24)29683
19.2%
Common
ValueCountFrequency (%)
18319
61.7%
12150
 
7.2%
21584
 
5.3%
31214
 
4.1%
51212
 
4.1%
41177
 
4.0%
6936
 
3.2%
7854
 
2.9%
8786
 
2.6%
9694
 
2.3%
Other values (3)765
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII184387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E31435
17.0%
T21508
11.7%
18319
9.9%
A13409
 
7.3%
R13127
 
7.1%
S13084
 
7.1%
N9934
 
5.4%
V6310
 
3.4%
U5958
 
3.2%
O5897
 
3.2%
Other values (37)45406
24.6%

BLOCK
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3987
Distinct (%)28.0%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3110.363222
Minimum1
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size111.6 KiB
2022-06-30T20:50:51.315695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile140
Q1837
median1886
Q34543
95-th percentile9903
Maximum99999
Range99998
Interquartile range (IQR)3706

Descriptive statistics

Standard deviation3867.934029
Coefficient of variation (CV)1.243563453
Kurtosis193.3490579
Mean3110.363222
Median Absolute Deviation (MAD)1327
Skewness8.727046272
Sum44366221
Variance14960913.65
MonotonicityNot monotonic
2022-06-30T20:50:51.617597image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54116
 
0.8%
445296
 
0.7%
117153
 
0.4%
529542
 
0.3%
458638
 
0.3%
97237
 
0.3%
1635036
 
0.3%
202332
 
0.2%
17131
 
0.2%
122229
 
0.2%
Other values (3977)13754
96.4%
ValueCountFrequency (%)
117
0.1%
29
0.1%
42
 
< 0.1%
616
0.1%
71
 
< 0.1%
112
 
< 0.1%
134
 
< 0.1%
1516
0.1%
1621
0.1%
175
 
< 0.1%
ValueCountFrequency (%)
999997
 
< 0.1%
1635036
0.3%
163406
 
< 0.1%
163191
 
< 0.1%
162941
 
< 0.1%
162852
 
< 0.1%
162741
 
< 0.1%
162641
 
< 0.1%
162421
 
< 0.1%
162311
 
< 0.1%

LOT
Real number (ℝ≥0)

Distinct367
Distinct (%)2.6%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1173.986748
Minimum0
Maximum9021
Zeros24
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size111.6 KiB
2022-06-30T20:50:51.910328image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q115
median38
Q386
95-th percentile7502
Maximum9021
Range9021
Interquartile range (IQR)71

Descriptive statistics

Standard deviation2669.251182
Coefficient of variation (CV)2.27366381
Kurtosis1.808049689
Mean1173.986748
Median Absolute Deviation (MAD)28
Skewness1.949326048
Sum16743399
Variance7124901.872
MonotonicityNot monotonic
2022-06-30T20:50:52.195053image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11349
 
9.5%
7501971
 
6.8%
7502501
 
3.5%
7503255
 
1.8%
10207
 
1.5%
6196
 
1.4%
5186
 
1.3%
7185
 
1.3%
29184
 
1.3%
21184
 
1.3%
Other values (357)10044
70.4%
ValueCountFrequency (%)
024
 
0.2%
11349
9.5%
2158
 
1.1%
3143
 
1.0%
4123
 
0.9%
5186
 
1.3%
6196
 
1.4%
7185
 
1.3%
8170
 
1.2%
9142
 
1.0%
ValueCountFrequency (%)
90211
 
< 0.1%
90015
 
< 0.1%
75171
 
< 0.1%
75142
 
< 0.1%
75136
 
< 0.1%
751214
0.1%
751118
0.1%
75108
 
0.1%
750912
0.1%
750826
0.2%

ZIP CODE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct202
Distinct (%)1.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean10738.76937
Minimum10001
Maximum11697
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size111.6 KiB
2022-06-30T20:50:52.455382image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum10001
5-th percentile10005
Q110027
median11101
Q311226
95-th percentile11418
Maximum11697
Range1696
Interquartile range (IQR)1199

Descriptive statistics

Standard deviation580.6583289
Coefficient of variation (CV)0.05407121702
Kurtosis-1.744492316
Mean10738.76937
Median Absolute Deviation (MAD)331
Skewness-0.1747590919
Sum153188545
Variance337164.0949
MonotonicityNot monotonic
2022-06-30T20:50:52.717378image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11101447
 
3.1%
11201440
 
3.1%
10013372
 
2.6%
10019271
 
1.9%
10011255
 
1.8%
10001249
 
1.7%
11211248
 
1.7%
10003236
 
1.7%
11217216
 
1.5%
11238215
 
1.5%
Other values (192)11316
79.3%
ValueCountFrequency (%)
10001249
1.7%
10002155
1.1%
10003236
1.7%
1000461
 
0.4%
1000544
 
0.3%
1000618
 
0.1%
1000769
 
0.5%
1000997
 
0.7%
10010162
1.1%
10011255
1.8%
ValueCountFrequency (%)
1169742
0.3%
1169427
 
0.2%
1169319
 
0.1%
1169232
 
0.2%
1169178
0.5%
1143619
 
0.1%
1143563
0.4%
11434100
0.7%
1143343
0.3%
1143283
0.6%

SUBMITTED DATE
Categorical

HIGH CARDINALITY

Distinct399
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size111.6 KiB
12/08/2021 12:00:00 AM
 
92
05/04/2021 12:00:00 AM
 
88
05/25/2021 12:00:00 AM
 
86
01/11/2022 12:00:00 AM
 
83
05/05/2021 12:00:00 AM
 
83
Other values (394)
13834 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters313852
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)0.2%

Sample

1st row01/25/2022 12:00:00 AM
2nd row01/27/2022 12:00:00 AM
3rd row05/03/2021 12:00:00 AM
4th row08/13/2021 12:00:00 AM
5th row11/16/2021 12:00:00 AM

Common Values

ValueCountFrequency (%)
12/08/2021 12:00:00 AM92
 
0.6%
05/04/2021 12:00:00 AM88
 
0.6%
05/25/2021 12:00:00 AM86
 
0.6%
01/11/2022 12:00:00 AM83
 
0.6%
05/05/2021 12:00:00 AM83
 
0.6%
12/20/2021 12:00:00 AM81
 
0.6%
12/21/2021 12:00:00 AM77
 
0.5%
03/10/2022 12:00:00 AM77
 
0.5%
05/12/2021 12:00:00 AM77
 
0.5%
05/10/2021 12:00:00 AM77
 
0.5%
Other values (389)13445
94.2%

Length

2022-06-30T20:50:53.002233image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
am14266
33.3%
12:00:0014266
33.3%
12/08/202192
 
0.2%
05/04/202188
 
0.2%
05/25/202186
 
0.2%
01/11/202283
 
0.2%
05/05/202183
 
0.2%
12/20/202181
 
0.2%
05/12/202177
 
0.2%
05/10/202177
 
0.2%
Other values (391)13599
31.8%

Most occurring characters

ValueCountFrequency (%)
089168
28.4%
255016
17.5%
136198
11.5%
/28532
 
9.1%
28532
 
9.1%
:28532
 
9.1%
A14266
 
4.5%
M14266
 
4.5%
33579
 
1.1%
43229
 
1.0%
Other values (5)12534
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number199724
63.6%
Other Punctuation57064
 
18.2%
Space Separator28532
 
9.1%
Uppercase Letter28532
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
089168
44.6%
255016
27.5%
136198
18.1%
33579
 
1.8%
43229
 
1.6%
52823
 
1.4%
82704
 
1.4%
92446
 
1.2%
72437
 
1.2%
62124
 
1.1%
Other Punctuation
ValueCountFrequency (%)
/28532
50.0%
:28532
50.0%
Uppercase Letter
ValueCountFrequency (%)
A14266
50.0%
M14266
50.0%
Space Separator
ValueCountFrequency (%)
28532
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common285320
90.9%
Latin28532
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
089168
31.3%
255016
19.3%
136198
12.7%
/28532
 
10.0%
28532
 
10.0%
:28532
 
10.0%
33579
 
1.3%
43229
 
1.1%
52823
 
1.0%
82704
 
0.9%
Other values (3)7007
 
2.5%
Latin
ValueCountFrequency (%)
A14266
50.0%
M14266
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII313852
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
089168
28.4%
255016
17.5%
136198
11.5%
/28532
 
9.1%
28532
 
9.1%
:28532
 
9.1%
A14266
 
4.5%
M14266
 
4.5%
33579
 
1.1%
43229
 
1.0%
Other values (5)12534
 
4.0%

C OF O STATUS
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size111.6 KiB
CO Issued
14266 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters128394
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCO Issued
2nd rowCO Issued
3rd rowCO Issued
4th rowCO Issued
5th rowCO Issued

Common Values

ValueCountFrequency (%)
CO Issued14266
100.0%

Length

2022-06-30T20:50:53.245327image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-30T20:50:53.476366image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
co14266
50.0%
issued14266
50.0%

Most occurring characters

ValueCountFrequency (%)
s28532
22.2%
C14266
11.1%
O14266
11.1%
14266
11.1%
I14266
11.1%
u14266
11.1%
e14266
11.1%
d14266
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter71330
55.6%
Uppercase Letter42798
33.3%
Space Separator14266
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s28532
40.0%
u14266
20.0%
e14266
20.0%
d14266
20.0%
Uppercase Letter
ValueCountFrequency (%)
C14266
33.3%
O14266
33.3%
I14266
33.3%
Space Separator
ValueCountFrequency (%)
14266
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin114128
88.9%
Common14266
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
s28532
25.0%
C14266
12.5%
O14266
12.5%
I14266
12.5%
u14266
12.5%
e14266
12.5%
d14266
12.5%
Common
ValueCountFrequency (%)
14266
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII128394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s28532
22.2%
C14266
11.1%
O14266
11.1%
14266
11.1%
I14266
11.1%
u14266
11.1%
e14266
11.1%
d14266
11.1%

C OF O SEQUENCE #
Real number (ℝ≥0)

Distinct14265
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9527.692486
Minimum13
Maximum19520
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size111.6 KiB
2022-06-30T20:50:53.605026image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile1348.25
Q14951.25
median9503.5
Q314006.5
95-th percentile17907.75
Maximum19520
Range19507
Interquartile range (IQR)9055.25

Descriptive statistics

Standard deviation5286.289072
Coefficient of variation (CV)0.5548341406
Kurtosis-1.151682768
Mean9527.692486
Median Absolute Deviation (MAD)4528.5
Skewness0.02737456492
Sum135922061
Variance27944852.16
MonotonicityNot monotonic
2022-06-30T20:50:53.889652image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
168332
 
< 0.1%
20491
 
< 0.1%
54001
 
< 0.1%
94701
 
< 0.1%
176661
 
< 0.1%
53841
 
< 0.1%
74331
 
< 0.1%
12901
 
< 0.1%
33391
 
< 0.1%
135801
 
< 0.1%
Other values (14255)14255
99.9%
ValueCountFrequency (%)
131
< 0.1%
151
< 0.1%
161
< 0.1%
171
< 0.1%
181
< 0.1%
191
< 0.1%
211
< 0.1%
241
< 0.1%
441
< 0.1%
451
< 0.1%
ValueCountFrequency (%)
195201
< 0.1%
195191
< 0.1%
195151
< 0.1%
194941
< 0.1%
194931
< 0.1%
194901
< 0.1%
194831
< 0.1%
194801
< 0.1%
194721
< 0.1%
194711
< 0.1%

C OF O FILING TYPE
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size111.6 KiB
Renewal Without Change
6822 
Final
4180 
Initial
2093 
Renewal With Change
1167 

Length

Max length22
Median length19
Mean length14.57074744
Min length5

Characters and Unicode

Total characters207808
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRenewal Without Change
2nd rowRenewal With Change
3rd rowRenewal With Change
4th rowRenewal Without Change
5th rowRenewal Without Change

Common Values

ValueCountFrequency (%)
Renewal Without Change6822
47.8%
Final4180
29.3%
Initial2093
 
14.7%
Renewal With Change1167
 
8.2%
(Missing)4
 
< 0.1%

Length

2022-06-30T20:50:54.155559image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-30T20:50:54.402100image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
renewal7989
26.4%
change7989
26.4%
without6822
22.6%
final4180
13.8%
initial2093
 
6.9%
with1167
 
3.9%

Most occurring characters

ValueCountFrequency (%)
e23967
11.5%
n22251
10.7%
a22251
10.7%
t16904
 
8.1%
i16355
 
7.9%
h15978
 
7.7%
15978
 
7.7%
l14262
 
6.9%
g7989
 
3.8%
C7989
 
3.8%
Other values (7)43884
21.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter161590
77.8%
Uppercase Letter30240
 
14.6%
Space Separator15978
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e23967
14.8%
n22251
13.8%
a22251
13.8%
t16904
10.5%
i16355
10.1%
h15978
9.9%
l14262
8.8%
g7989
 
4.9%
w7989
 
4.9%
o6822
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
C7989
26.4%
R7989
26.4%
W7989
26.4%
F4180
13.8%
I2093
 
6.9%
Space Separator
ValueCountFrequency (%)
15978
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin191830
92.3%
Common15978
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e23967
12.5%
n22251
11.6%
a22251
11.6%
t16904
8.8%
i16355
8.5%
h15978
8.3%
l14262
 
7.4%
g7989
 
4.2%
C7989
 
4.2%
R7989
 
4.2%
Other values (6)35895
18.7%
Common
ValueCountFrequency (%)
15978
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII207808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e23967
11.5%
n22251
10.7%
a22251
10.7%
t16904
 
8.1%
i16355
 
7.9%
h15978
 
7.7%
15978
 
7.7%
l14262
 
6.9%
g7989
 
3.8%
C7989
 
3.8%
Other values (7)43884
21.1%

COMMUNITY BOARD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct66
Distinct (%)0.5%
Missing19
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean268.6559978
Minimum1
Maximum503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size111.6 KiB
2022-06-30T20:50:54.539749image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile102
Q1107
median303
Q3402
95-th percentile502
Maximum503
Range502
Interquartile range (IQR)295

Descriptive statistics

Standard deviation131.1112884
Coefficient of variation (CV)0.4880266567
Kurtosis-1.249806209
Mean268.6559978
Median Absolute Deviation (MAD)104
Skewness0.01277242913
Sum3827542
Variance17190.16995
MonotonicityNot monotonic
2022-06-30T20:50:54.825567image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1051036
 
7.3%
302703
 
4.9%
301683
 
4.8%
102539
 
3.8%
101526
 
3.7%
104520
 
3.6%
402445
 
3.1%
108432
 
3.0%
407431
 
3.0%
303422
 
3.0%
Other values (56)8510
59.7%
ValueCountFrequency (%)
11
 
< 0.1%
23
 
< 0.1%
33
 
< 0.1%
42
 
< 0.1%
101526
3.7%
102539
3.8%
103300
 
2.1%
104520
3.6%
1051036
7.3%
106313
 
2.2%
ValueCountFrequency (%)
503407
2.9%
502311
2.2%
501259
1.8%
4832
 
< 0.1%
4817
 
< 0.1%
414184
1.3%
413169
1.2%
412296
2.1%
411246
1.7%
410117
 
0.8%

C OF O ISSUANCE DATE
Categorical

HIGH CARDINALITY
UNIFORM

Distinct14258
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size111.6 KiB
08/20/21 2:57:36 PM
 
2
10/20/21 3:50:05 PM
 
2
03/15/22 9:19:56 AM
 
2
04/27/22 4:14:34 PM
 
2
03/23/22 12:37:03 PM
 
2
Other values (14253)
14256 

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters285320
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14250 ?
Unique (%)99.9%

Sample

1st row01/26/22 3:49:10 PM
2nd row03/17/22 10:17:02 AM
3rd row05/27/21 3:51:49 PM
4th row08/20/21 3:25:28 PM
5th row11/24/21 9:58:25 AM

Common Values

ValueCountFrequency (%)
08/20/21 2:57:36 PM2
 
< 0.1%
10/20/21 3:50:05 PM2
 
< 0.1%
03/15/22 9:19:56 AM2
 
< 0.1%
04/27/22 4:14:34 PM2
 
< 0.1%
03/23/22 12:37:03 PM2
 
< 0.1%
05/05/22 11:02:03 AM2
 
< 0.1%
12/01/21 2:12:15 PM2
 
< 0.1%
12/21/21 10:43:29 AM2
 
< 0.1%
05/10/21 11:23:55 AM1
 
< 0.1%
05/06/22 4:45:52 PM1
 
< 0.1%
Other values (14248)14248
99.9%

Length

2022-06-30T20:50:55.095351image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pm9183
 
21.5%
am5083
 
11.9%
07/19/2199
 
0.2%
12/21/2199
 
0.2%
10/01/2196
 
0.2%
09/29/2192
 
0.2%
10/05/2187
 
0.2%
01/19/2287
 
0.2%
12/22/2187
 
0.2%
11/09/2186
 
0.2%
Other values (11813)27799
65.0%

Most occurring characters

ValueCountFrequency (%)
239066
13.7%
37580
13.2%
137252
13.1%
/28532
10.0%
:28532
10.0%
026631
9.3%
M14266
 
5.0%
313251
 
4.6%
411926
 
4.2%
510689
 
3.7%
Other values (6)37595
13.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number162144
56.8%
Other Punctuation57064
 
20.0%
Space Separator37580
 
13.2%
Uppercase Letter28532
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
239066
24.1%
137252
23.0%
026631
16.4%
313251
 
8.2%
411926
 
7.4%
510689
 
6.6%
96828
 
4.2%
85902
 
3.6%
75322
 
3.3%
65277
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
M14266
50.0%
P9183
32.2%
A5083
 
17.8%
Other Punctuation
ValueCountFrequency (%)
/28532
50.0%
:28532
50.0%
Space Separator
ValueCountFrequency (%)
37580
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common256788
90.0%
Latin28532
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
239066
15.2%
37580
14.6%
137252
14.5%
/28532
11.1%
:28532
11.1%
026631
10.4%
313251
 
5.2%
411926
 
4.6%
510689
 
4.2%
96828
 
2.7%
Other values (3)16501
6.4%
Latin
ValueCountFrequency (%)
M14266
50.0%
P9183
32.2%
A5083
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII285320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
239066
13.7%
37580
13.2%
137252
13.1%
/28532
10.0%
:28532
10.0%
026631
9.3%
M14266
 
5.0%
313251
 
4.6%
411926
 
4.2%
510689
 
3.7%
Other values (6)37595
13.2%

APPLICATION NUMBER
Categorical

HIGH CARDINALITY
UNIFORM

Distinct14265
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size111.6 KiB
CO-000016833
 
2
CO-000013781
 
1
CO-000012337
 
1
CO-000010264
 
1
CO-000008973
 
1
Other values (14260)
14260 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters171192
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14264 ?
Unique (%)> 99.9%

Sample

1st rowCO-000014504
2nd rowCO-000014626
3rd rowCO-000002499
4th rowCO-000006765
5th rowCO-000011434

Common Values

ValueCountFrequency (%)
CO-0000168332
 
< 0.1%
CO-0000137811
 
< 0.1%
CO-0000123371
 
< 0.1%
CO-0000102641
 
< 0.1%
CO-0000089731
 
< 0.1%
CO-0000054131
 
< 0.1%
CO-0000157151
 
< 0.1%
CO-0000089491
 
< 0.1%
CO-0000132321
 
< 0.1%
CO-0000020541
 
< 0.1%
Other values (14255)14255
99.9%

Length

2022-06-30T20:50:55.328964image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
co-0000168332
 
< 0.1%
co-0000034031
 
< 0.1%
co-0000192051
 
< 0.1%
co-0000054961
 
< 0.1%
co-0000033391
 
< 0.1%
co-0000039551
 
< 0.1%
co-0000171971
 
< 0.1%
co-0000074481
 
< 0.1%
co-0000163851
 
< 0.1%
co-0000069361
 
< 0.1%
Other values (14255)14255
99.9%

Most occurring characters

ValueCountFrequency (%)
070138
41.0%
C14266
 
8.3%
O14266
 
8.3%
-14266
 
8.3%
112536
 
7.3%
35905
 
3.4%
25903
 
3.4%
45881
 
3.4%
65777
 
3.4%
55764
 
3.4%
Other values (3)16490
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number128394
75.0%
Uppercase Letter28532
 
16.7%
Dash Punctuation14266
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
070138
54.6%
112536
 
9.8%
35905
 
4.6%
25903
 
4.6%
45881
 
4.6%
65777
 
4.5%
55764
 
4.5%
75682
 
4.4%
85642
 
4.4%
95166
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
C14266
50.0%
O14266
50.0%
Dash Punctuation
ValueCountFrequency (%)
-14266
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common142660
83.3%
Latin28532
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
070138
49.2%
-14266
 
10.0%
112536
 
8.8%
35905
 
4.1%
25903
 
4.1%
45881
 
4.1%
65777
 
4.0%
55764
 
4.0%
75682
 
4.0%
85642
 
4.0%
Latin
ValueCountFrequency (%)
C14266
50.0%
O14266
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII171192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
070138
41.0%
C14266
 
8.3%
O14266
 
8.3%
-14266
 
8.3%
112536
 
7.3%
35905
 
3.4%
25903
 
3.4%
45881
 
3.4%
65777
 
3.4%
55764
 
3.4%
Other values (3)16490
 
9.6%

xCoordinate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6793
Distinct (%)48.0%
Missing121
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean998590.134
Minimum914661
Maximum1066784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size111.6 KiB
2022-06-30T20:50:55.581709image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum914661
5-th percentile957653
Q1987029
median995560
Q31009269
95-th percentile1043093.4
Maximum1066784
Range152123
Interquartile range (IQR)22240

Descriptive statistics

Standard deviation23487.76873
Coefficient of variation (CV)0.02352093009
Kurtosis1.560564711
Mean998590.134
Median Absolute Deviation (MAD)9642
Skewness-0.08034021147
Sum1.412505745 × 1010
Variance551675280.1
MonotonicityNot monotonic
2022-06-30T20:50:55.838759image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98812812
 
0.1%
98831912
 
0.1%
99595011
 
0.1%
98726411
 
0.1%
98695310
 
0.1%
98887010
 
0.1%
98723410
 
0.1%
9989469
 
0.1%
9951849
 
0.1%
10090229
 
0.1%
Other values (6783)14042
98.4%
(Missing)121
 
0.8%
ValueCountFrequency (%)
9146611
< 0.1%
9149731
< 0.1%
9152011
< 0.1%
9153451
< 0.1%
9153591
< 0.1%
9156751
< 0.1%
9162111
< 0.1%
9162652
< 0.1%
9162682
< 0.1%
9162891
< 0.1%
ValueCountFrequency (%)
10667841
 
< 0.1%
10666451
 
< 0.1%
10665051
 
< 0.1%
10664941
 
< 0.1%
10664541
 
< 0.1%
10661831
 
< 0.1%
10657153
< 0.1%
10656861
 
< 0.1%
10655272
< 0.1%
10654011
 
< 0.1%

yCoordinate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6872
Distinct (%)48.6%
Missing121
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean200661.1704
Minimum121245
Maximum271410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size111.6 KiB
2022-06-30T20:50:56.122984image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum121245
5-th percentile153589.4
Q1186267
median202642
Q3215876
95-th percentile244974.4
Maximum271410
Range150165
Interquartile range (IQR)29609

Descriptive statistics

Standard deviation26118.14057
Coefficient of variation (CV)0.1301604118
Kurtosis0.2426639918
Mean200661.1704
Median Absolute Deviation (MAD)14566
Skewness-0.2382758943
Sum2838352256
Variance682157266.7
MonotonicityNot monotonic
2022-06-30T20:50:57.015522image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19330512
 
0.1%
21387611
 
0.1%
21595911
 
0.1%
19127511
 
0.1%
21468710
 
0.1%
19454710
 
0.1%
19726610
 
0.1%
19055510
 
0.1%
20204710
 
0.1%
15756610
 
0.1%
Other values (6862)14040
98.4%
(Missing)121
 
0.8%
ValueCountFrequency (%)
1212451
< 0.1%
1213032
< 0.1%
1213611
< 0.1%
1215071
< 0.1%
1217562
< 0.1%
1221821
< 0.1%
1223681
< 0.1%
1228751
< 0.1%
1228911
< 0.1%
1231451
< 0.1%
ValueCountFrequency (%)
2714101
< 0.1%
2707791
< 0.1%
2704052
< 0.1%
2694591
< 0.1%
2693252
< 0.1%
2690891
< 0.1%
2688581
< 0.1%
2688411
< 0.1%
2684791
< 0.1%
2684381
< 0.1%

latitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7048
Distinct (%)49.8%
Missing121
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean40.71740246
Minimum40.499212
Maximum40.91159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size111.6 KiB
2022-06-30T20:50:57.315775image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum40.499212
5-th percentile40.588132
Q140.677929
median40.722874
Q340.759115
95-th percentile40.8389676
Maximum40.91159
Range0.412378
Interquartile range (IQR)0.081186

Descriptive statistics

Standard deviation0.07169515298
Coefficient of variation (CV)0.00176079879
Kurtosis0.2441285577
Mean40.71740246
Median Absolute Deviation (MAD)0.039987
Skewness-0.2393437313
Sum575947.6578
Variance0.00514019496
MonotonicityNot monotonic
2022-06-30T20:50:57.598642image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.75594214
 
0.1%
40.69725412
 
0.1%
40.69168211
 
0.1%
40.59915810
 
0.1%
40.72124810
 
0.1%
40.7569789
 
0.1%
40.6920699
 
0.1%
40.746998
 
0.1%
40.6951988
 
0.1%
40.7469798
 
0.1%
Other values (7038)14046
98.5%
(Missing)121
 
0.8%
ValueCountFrequency (%)
40.4992121
< 0.1%
40.4993712
< 0.1%
40.4995321
< 0.1%
40.4999371
< 0.1%
40.500622
< 0.1%
40.5017821
< 0.1%
40.5022941
< 0.1%
40.5036831
< 0.1%
40.5037271
< 0.1%
40.5044461
< 0.1%
ValueCountFrequency (%)
40.911591
< 0.1%
40.9098041
< 0.1%
40.9088372
< 0.1%
40.9061771
< 0.1%
40.9058152
< 0.1%
40.9052211
< 0.1%
40.904591
< 0.1%
40.9045431
< 0.1%
40.9035531
< 0.1%
40.9034281
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7045
Distinct (%)49.8%
Missing121
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean-73.94823164
Minimum-74.250237
Maximum-73.702122
Zeros0
Zeros (%)0.0%
Negative14145
Negative (%)99.2%
Memory size111.6 KiB
2022-06-30T20:50:57.858936image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-74.250237
5-th percentile-74.095761
Q1-73.989972
median-73.959151
Q3-73.909658
95-th percentile-73.7877684
Maximum-73.702122
Range0.548115
Interquartile range (IQR)0.080314

Descriptive statistics

Standard deviation0.08468585412
Coefficient of variation (CV)-0.001145204588
Kurtosis1.549373853
Mean-73.94823164
Median Absolute Deviation (MAD)0.034827
Skewness-0.07613728974
Sum-1045997.737
Variance0.007171693888
MonotonicityNot monotonic
2022-06-30T20:50:58.113210image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.98601512
 
0.1%
-73.94950712
 
0.1%
-73.98913211
 
0.1%
-73.97996510
 
0.1%
-73.98925510
 
0.1%
-73.98332410
 
0.1%
-73.98658710
 
0.1%
-73.98579810
 
0.1%
-73.9831359
 
0.1%
-73.9490069
 
0.1%
Other values (7035)14042
98.4%
(Missing)121
 
0.8%
ValueCountFrequency (%)
-74.2502371
< 0.1%
-74.2491251
< 0.1%
-74.2483071
< 0.1%
-74.2478051
< 0.1%
-74.2477551
< 0.1%
-74.2465931
< 0.1%
-74.2446651
< 0.1%
-74.2444842
< 0.1%
-74.244482
< 0.1%
-74.2444041
< 0.1%
ValueCountFrequency (%)
-73.7021221
 
< 0.1%
-73.7026411
 
< 0.1%
-73.7031681
 
< 0.1%
-73.7031751
 
< 0.1%
-73.7033221
 
< 0.1%
-73.7043121
 
< 0.1%
-73.7059613
< 0.1%
-73.7061661
 
< 0.1%
-73.7066982
< 0.1%
-73.707171
 
< 0.1%

communityDistrict
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct62
Distinct (%)0.4%
Missing121
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean267.9300813
Minimum101
Maximum503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size111.6 KiB
2022-06-30T20:50:58.409628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile102
Q1107
median302
Q3402
95-th percentile501
Maximum503
Range402
Interquartile range (IQR)295

Descriptive statistics

Standard deviation130.320782
Coefficient of variation (CV)0.4863984713
Kurtosis-1.25653878
Mean267.9300813
Median Absolute Deviation (MAD)105
Skewness0.01056934867
Sum3789871
Variance16983.50621
MonotonicityNot monotonic
2022-06-30T20:50:58.709654image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1051035
 
7.3%
302703
 
4.9%
301679
 
4.8%
102540
 
3.8%
101522
 
3.7%
104519
 
3.6%
402443
 
3.1%
407435
 
3.0%
108431
 
3.0%
303422
 
3.0%
Other values (52)8416
59.0%
ValueCountFrequency (%)
101522
3.7%
102540
3.8%
103293
 
2.1%
104519
3.6%
1051035
7.3%
106312
 
2.2%
107376
 
2.6%
108431
3.0%
109106
 
0.7%
110202
 
1.4%
ValueCountFrequency (%)
503371
2.6%
502296
2.1%
501242
1.7%
4833
 
< 0.1%
4817
 
< 0.1%
414198
1.4%
413169
1.2%
412296
2.1%
411246
1.7%
410117
 
0.8%

communityDistrictBoroughCode
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing121
Missing (%)0.8%
Memory size111.6 KiB
1.0
4528 
3.0
4512 
4.0
3030 
2.0
1166 
5.0
909 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters42435
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.04528
31.7%
3.04512
31.6%
4.03030
21.2%
2.01166
 
8.2%
5.0909
 
6.4%
(Missing)121
 
0.8%

Length

2022-06-30T20:50:59.020256image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-30T20:50:59.283394image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1.04528
32.0%
3.04512
31.9%
4.03030
21.4%
2.01166
 
8.2%
5.0909
 
6.4%

Most occurring characters

ValueCountFrequency (%)
.14145
33.3%
014145
33.3%
14528
 
10.7%
34512
 
10.6%
43030
 
7.1%
21166
 
2.7%
5909
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28290
66.7%
Other Punctuation14145
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014145
50.0%
14528
 
16.0%
34512
 
15.9%
43030
 
10.7%
21166
 
4.1%
5909
 
3.2%
Other Punctuation
ValueCountFrequency (%)
.14145
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common42435
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.14145
33.3%
014145
33.3%
14528
 
10.7%
34512
 
10.6%
43030
 
7.1%
21166
 
2.7%
5909
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII42435
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.14145
33.3%
014145
33.3%
14528
 
10.7%
34512
 
10.6%
43030
 
7.1%
21166
 
2.7%
5909
 
2.1%

communityDistrictNumber
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)0.1%
Missing121
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean5.922304701
Minimum1
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size111.6 KiB
2022-06-30T20:50:59.409911image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q38
95-th percentile14
Maximum83
Range82
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.662231008
Coefficient of variation (CV)0.7872325459
Kurtosis49.61157375
Mean5.922304701
Median Absolute Deviation (MAD)3
Skewness3.71348615
Sum83771
Variance21.73639797
MonotonicityNot monotonic
2022-06-30T20:50:59.654458image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
22047
14.3%
11957
13.7%
51578
11.1%
31268
8.9%
71100
7.7%
41017
7.1%
6895
6.3%
8881
6.2%
12802
 
5.6%
11573
 
4.0%
Other values (11)2027
14.2%
ValueCountFrequency (%)
11957
13.7%
22047
14.3%
31268
8.9%
41017
7.1%
51578
11.1%
6895
6.3%
71100
7.7%
8881
6.2%
9397
 
2.8%
10517
 
3.6%
ValueCountFrequency (%)
833
 
< 0.1%
817
 
< 0.1%
552
 
< 0.1%
1896
 
0.7%
17118
 
0.8%
1699
 
0.7%
15158
 
1.1%
14366
2.6%
13264
 
1.9%
12802
5.6%

cityCouncilDistrict
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct51
Distinct (%)0.4%
Missing121
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean23.05146695
Minimum1
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size111.6 KiB
2022-06-30T20:50:59.899871image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median25
Q336
95-th percentile50
Maximum51
Range50
Interquartile range (IQR)31

Descriptive statistics

Standard deviation16.10849574
Coefficient of variation (CV)0.698805667
Kurtosis-1.353713911
Mean23.05146695
Median Absolute Deviation (MAD)14
Skewness0.04533145544
Sum326063
Variance259.4836349
MonotonicityNot monotonic
2022-06-30T20:51:00.176954image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31035
 
7.3%
4997
 
7.0%
33935
 
6.6%
1895
 
6.3%
26553
 
3.9%
2485
 
3.4%
34472
 
3.3%
39416
 
2.9%
35404
 
2.8%
36395
 
2.8%
Other values (41)7558
53.0%
ValueCountFrequency (%)
1895
6.3%
2485
3.4%
31035
7.3%
4997
7.0%
5237
 
1.7%
6339
 
2.4%
7128
 
0.9%
8221
 
1.5%
9267
 
1.9%
1072
 
0.5%
ValueCountFrequency (%)
51361
2.5%
50351
2.5%
49197
1.4%
48167
1.2%
47149
1.0%
4660
 
0.4%
45125
 
0.9%
44214
1.5%
43115
 
0.8%
42265
1.9%

censusTract2010
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1076
Distinct (%)7.6%
Missing121
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean6140.341534
Minimum1
Maximum157903
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size111.6 KiB
2022-06-30T20:51:00.433556image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19
Q191
median235
Q3737
95-th percentile36902
Maximum157903
Range157902
Interquartile range (IQR)646

Descriptive statistics

Standard deviation20737.84482
Coefficient of variation (CV)3.377311296
Kurtosis25.3592508
Mean6140.341534
Median Absolute Deviation (MAD)190
Skewness4.809769071
Sum86855131
Variance430058207.9
MonotonicityNot monotonic
2022-06-30T20:51:00.717266image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7208
 
1.5%
21176
 
1.2%
33172
 
1.2%
37155
 
1.1%
99136
 
1.0%
1070119
 
0.8%
19114
 
0.8%
3999
 
0.7%
4193
 
0.7%
13789
 
0.6%
Other values (1066)12784
89.6%
(Missing)121
 
0.8%
ValueCountFrequency (%)
179
 
0.6%
24
 
< 0.1%
35
 
< 0.1%
44
 
< 0.1%
511
 
0.1%
615
 
0.1%
7208
1.5%
830
 
0.2%
973
 
0.5%
1133
 
0.2%
ValueCountFrequency (%)
1579038
0.1%
1579022
 
< 0.1%
15790111
0.1%
1571023
 
< 0.1%
1571015
 
< 0.1%
15510212
0.1%
15290215
0.1%
1529014
 
< 0.1%
15070218
0.1%
15070110
0.1%

buildingIdentificationNumber
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6140
Distinct (%)45.2%
Missing672
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean2692485.571
Minimum1000000
Maximum5174460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size111.6 KiB
2022-06-30T20:51:01.020285image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1000000
5-th percentile1005457.05
Q11082882
median3028784.5
Q34000000
95-th percentile4622970.35
Maximum5174460
Range4174460
Interquartile range (IQR)2917118

Descriptive statistics

Standard deviation1346639.023
Coefficient of variation (CV)0.5001471641
Kurtosis-1.348435421
Mean2692485.571
Median Absolute Deviation (MAD)1100558
Skewness0.01078959736
Sum3.660164885 × 1010
Variance1.813436657 × 1012
MonotonicityNot monotonic
2022-06-30T20:51:01.309959image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000000605
 
4.2%
4000000453
 
3.2%
1000000229
 
1.6%
2000000187
 
1.3%
5000000166
 
1.2%
333588414
 
0.1%
300008813
 
0.1%
300041711
 
0.1%
212842510
 
0.1%
300017110
 
0.1%
Other values (6130)11896
83.4%
(Missing)672
 
4.7%
ValueCountFrequency (%)
1000000229
1.6%
10000035
 
< 0.1%
10000052
 
< 0.1%
10000372
 
< 0.1%
10000454
 
< 0.1%
10000572
 
< 0.1%
10000581
 
< 0.1%
10000606
 
< 0.1%
10007974
 
< 0.1%
10008094
 
< 0.1%
ValueCountFrequency (%)
51744601
 
< 0.1%
51744423
< 0.1%
51717461
 
< 0.1%
51717451
 
< 0.1%
51717431
 
< 0.1%
51714801
 
< 0.1%
51714791
 
< 0.1%
51713891
 
< 0.1%
51713881
 
< 0.1%
51711331
 
< 0.1%

bbl
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6719
Distinct (%)49.4%
Missing672
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean2594417254
Minimum0
Maximum5080460194
Zeros25
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size111.6 KiB
2022-06-30T20:51:01.591423image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1003507503
Q11013030014
median3012750007
Q34000823128
95-th percentile4163500400
Maximum5080460194
Range5080460194
Interquartile range (IQR)2987793114

Descriptive statistics

Standard deviation1298947155
Coefficient of variation (CV)0.5006701036
Kurtosis-1.264114282
Mean2594417254
Median Absolute Deviation (MAD)1028940018
Skewness0.02456380133
Sum3.526850815 × 1013
Variance1.687263712 × 1018
MonotonicityNot monotonic
2022-06-30T20:51:01.862529image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
304452040050
 
0.4%
100972000137
 
0.3%
416350040029
 
0.2%
025
 
0.2%
302023005017
 
0.1%
302023000115
 
0.1%
300118000614
 
0.1%
403810035013
 
0.1%
300038000113
 
0.1%
301808750312
 
0.1%
Other values (6709)13369
93.7%
(Missing)672
 
4.7%
ValueCountFrequency (%)
025
0.2%
100001001011
0.1%
10000200025
 
< 0.1%
10000475012
 
< 0.1%
10001100172
 
< 0.1%
10001300274
 
< 0.1%
10001575018
 
0.1%
10001575023
 
< 0.1%
10001601202
 
< 0.1%
10001601251
 
< 0.1%
ValueCountFrequency (%)
50804601941
< 0.1%
50804600941
< 0.1%
50804600902
< 0.1%
50802601181
< 0.1%
50802100191
< 0.1%
50793000081
< 0.1%
50792900151
< 0.1%
50792800851
< 0.1%
50791200831
< 0.1%
50791100281
< 0.1%

nta
Categorical

HIGH CARDINALITY

Distinct193
Distinct (%)1.4%
Missing121
Missing (%)0.8%
Memory size111.6 KiB
MN17
 
700
MN13
 
539
MN24
 
535
QN31
 
404
BK38
 
371
Other values (188)
11596 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters56580
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMN25
2nd rowMN25
3rd rowMN25
4th rowMN25
5th rowMN25

Common Values

ValueCountFrequency (%)
MN17700
 
4.9%
MN13539
 
3.8%
MN24535
 
3.8%
QN31404
 
2.8%
BK38371
 
2.6%
BK73291
 
2.0%
MN23276
 
1.9%
BK37256
 
1.8%
MN25240
 
1.7%
MN12219
 
1.5%
Other values (183)10314
72.3%

Length

2022-06-30T20:51:02.113853image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mn17700
 
4.9%
mn13539
 
3.8%
mn24535
 
3.8%
qn31404
 
2.9%
bk38371
 
2.6%
bk73291
 
2.1%
mn23276
 
2.0%
bk37256
 
1.8%
mn25240
 
1.7%
mn12219
 
1.5%
Other values (183)10314
72.9%

Most occurring characters

ValueCountFrequency (%)
N7564
13.4%
B5672
10.0%
M4534
 
8.0%
14534
 
8.0%
K4512
 
8.0%
34451
 
7.9%
24208
 
7.4%
73109
 
5.5%
Q3030
 
5.4%
42605
 
4.6%
Other values (8)12361
21.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter28290
50.0%
Decimal Number28290
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
14534
16.0%
34451
15.7%
24208
14.9%
73109
11.0%
42605
9.2%
52205
7.8%
02040
7.2%
81995
7.1%
61792
 
6.3%
91351
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
N7564
26.7%
B5672
20.0%
M4534
16.0%
K4512
15.9%
Q3030
10.7%
X1160
 
4.1%
S909
 
3.2%
I909
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Latin28290
50.0%
Common28290
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
14534
16.0%
34451
15.7%
24208
14.9%
73109
11.0%
42605
9.2%
52205
7.8%
02040
7.2%
81995
7.1%
61792
 
6.3%
91351
 
4.8%
Latin
ValueCountFrequency (%)
N7564
26.7%
B5672
20.0%
M4534
16.0%
K4512
15.9%
Q3030
10.7%
X1160
 
4.1%
S909
 
3.2%
I909
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII56580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N7564
13.4%
B5672
10.0%
M4534
 
8.0%
14534
 
8.0%
K4512
 
8.0%
34451
 
7.9%
24208
 
7.4%
73109
 
5.5%
Q3030
 
5.4%
42605
 
4.6%
Other values (8)12361
21.8%

ntaName
Categorical

HIGH CARDINALITY

Distinct193
Distinct (%)1.4%
Missing121
Missing (%)0.8%
Memory size111.6 KiB
Midtown-Midtown South
 
700
Hudson Yards-Chelsea-Flatiron-Union Square
 
539
SoHo-TriBeCa-Civic Center-Little Italy
 
535
Hunters Point-Sunnyside-West Maspeth
 
404
DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill
 
371
Other values (188)
11596 

Length

Max length56
Median length39
Mean length21.61102863
Min length6

Characters and Unicode

Total characters305688
Distinct characters55
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBattery Park City-Lower Manhattan
2nd rowBattery Park City-Lower Manhattan
3rd rowBattery Park City-Lower Manhattan
4th rowBattery Park City-Lower Manhattan
5th rowBattery Park City-Lower Manhattan

Common Values

ValueCountFrequency (%)
Midtown-Midtown South700
 
4.9%
Hudson Yards-Chelsea-Flatiron-Union Square539
 
3.8%
SoHo-TriBeCa-Civic Center-Little Italy535
 
3.8%
Hunters Point-Sunnyside-West Maspeth404
 
2.8%
DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill371
 
2.6%
North Side-South Side291
 
2.0%
West Village276
 
1.9%
Park Slope-Gowanus256
 
1.8%
Battery Park City-Lower Manhattan240
 
1.7%
Upper West Side219
 
1.5%
Other values (183)10314
72.3%

Length

2022-06-30T20:51:02.388914image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
east1311
 
3.9%
south1264
 
3.8%
park1166
 
3.5%
hill1067
 
3.2%
north878
 
2.6%
west761
 
2.3%
heights735
 
2.2%
square720
 
2.2%
midtown-midtown700
 
2.1%
village594
 
1.8%
Other values (255)24153
72.4%

Most occurring characters

ValueCountFrequency (%)
e24759
 
8.1%
o21067
 
6.9%
t20707
 
6.8%
19204
 
6.3%
a18899
 
6.2%
n18256
 
6.0%
i18222
 
6.0%
r17834
 
5.8%
l16341
 
5.3%
s13837
 
4.5%
Other values (45)116562
38.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter225897
73.9%
Uppercase Letter48180
 
15.8%
Space Separator19204
 
6.3%
Dash Punctuation11994
 
3.9%
Other Punctuation277
 
0.1%
Open Punctuation68
 
< 0.1%
Close Punctuation68
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e24759
11.0%
o21067
9.3%
t20707
9.2%
a18899
 
8.4%
n18256
 
8.1%
i18222
 
8.1%
r17834
 
7.9%
l16341
 
7.2%
s13837
 
6.1%
d8641
 
3.8%
Other values (15)47334
21.0%
Uppercase Letter
ValueCountFrequency (%)
H5904
12.3%
S5840
12.1%
B5043
 
10.5%
C4832
 
10.0%
M3923
 
8.1%
P2722
 
5.6%
E1998
 
4.1%
W1935
 
4.0%
N1674
 
3.5%
L1559
 
3.2%
Other values (14)12750
26.5%
Other Punctuation
ValueCountFrequency (%)
.160
57.8%
'117
42.2%
Space Separator
ValueCountFrequency (%)
19204
100.0%
Dash Punctuation
ValueCountFrequency (%)
-11994
100.0%
Open Punctuation
ValueCountFrequency (%)
(68
100.0%
Close Punctuation
ValueCountFrequency (%)
)68
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin274077
89.7%
Common31611
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e24759
 
9.0%
o21067
 
7.7%
t20707
 
7.6%
a18899
 
6.9%
n18256
 
6.7%
i18222
 
6.6%
r17834
 
6.5%
l16341
 
6.0%
s13837
 
5.0%
d8641
 
3.2%
Other values (39)95514
34.8%
Common
ValueCountFrequency (%)
19204
60.8%
-11994
37.9%
.160
 
0.5%
'117
 
0.4%
(68
 
0.2%
)68
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII305688
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e24759
 
8.1%
o21067
 
6.9%
t20707
 
6.8%
19204
 
6.3%
a18899
 
6.2%
n18256
 
6.0%
i18222
 
6.0%
r17834
 
5.8%
l16341
 
5.3%
s13837
 
4.5%
Other values (45)116562
38.1%

Interactions

2022-06-30T20:50:42.849778image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:02.993136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:05.423557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:07.985537image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:10.564231image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:13.202073image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:15.924695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2022-06-30T20:50:44.747474image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:04.976394image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:07.511543image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:10.145904image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:12.697202image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:15.422844image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:17.964706image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:20.626800image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:23.738674image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:26.296213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:28.854792image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:31.523427image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:34.059326image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:36.615912image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:39.809082image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:42.383719image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:44.919603image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:05.138256image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:07.675460image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:10.298333image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:12.881896image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:15.591819image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:18.140378image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:20.798875image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:23.923156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:26.461803image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:29.026591image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:31.709728image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:34.227249image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:37.252402image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:39.986308image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:42.549265image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:45.052180image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:05.283941image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:07.830654image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:10.429303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:13.041608image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:15.757074image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:18.292629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:20.963265image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:24.083400image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:26.616140image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:29.174979image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:31.866425image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:34.379288image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:37.411029image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:40.151151image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-30T20:50:42.699733image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2022-06-30T20:51:02.655332image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-30T20:51:02.930895image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-30T20:51:03.214788image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-30T20:51:03.467331image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-30T20:51:03.655507image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-30T20:50:45.378665image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-30T20:50:47.016428image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-30T20:50:47.909939image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-30T20:50:48.419273image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

JOB FILING NAMEJOB TYPEBINBOROUGHHOUSE NOSTREET NAMEBLOCKLOTZIP CODESUBMITTED DATEC OF O STATUSC OF O SEQUENCE #C OF O FILING TYPECOMMUNITY BOARDC OF O ISSUANCE DATEAPPLICATION NUMBERxCoordinateyCoordinatelatitudelongitudecommunityDistrictcommunityDistrictBoroughCodecommunityDistrictNumbercityCouncilDistrictcensusTract2010buildingIdentificationNumberbblntantaName
001ALTERATION TYPE 11000003MANHATTAN10SOUTH STREET2.02.010004.001/25/2022 12:00:00 AMCO Issued14504Renewal Without Change101.001/26/22 3:49:10 PMCO-000014504981025.0194923.040.701695-74.011631101.01.01.01.09.01000003.01.000020e+09MN25Battery Park City-Lower Manhattan
101ALTERATION TYPE 11000003MANHATTAN10SOUTH STREET2.02.010004.001/27/2022 12:00:00 AMCO Issued14626Renewal With Change101.003/17/22 10:17:02 AMCO-000014626981025.0194923.040.701695-74.011631101.01.01.01.09.01000003.01.000020e+09MN25Battery Park City-Lower Manhattan
201ALTERATION TYPE 11000003MANHATTAN10SOUTH STREET2.02.010004.005/03/2021 12:00:00 AMCO Issued2499Renewal With Change101.005/27/21 3:51:49 PMCO-000002499981025.0194923.040.701695-74.011631101.01.01.01.09.01000003.01.000020e+09MN25Battery Park City-Lower Manhattan
301ALTERATION TYPE 11000003MANHATTAN10SOUTH STREET2.02.010004.008/13/2021 12:00:00 AMCO Issued6765Renewal Without Change101.008/20/21 3:25:28 PMCO-000006765981025.0194923.040.701695-74.011631101.01.01.01.09.01000003.01.000020e+09MN25Battery Park City-Lower Manhattan
401ALTERATION TYPE 11000003MANHATTAN10SOUTH STREET2.02.010004.011/16/2021 12:00:00 AMCO Issued11434Renewal Without Change101.011/24/21 9:58:25 AMCO-000011434981025.0194923.040.701695-74.011631101.01.01.01.09.01000003.01.000020e+09MN25Battery Park City-Lower Manhattan
501ALTERATION TYPE 11000005MANHATTAN1NEW YORK PLAZA4.07501.010004.004/13/2021 12:00:00 AMCO Issued1679Final101.008/27/21 10:03:44 AMCO-000001679980767.0195231.040.702540-74.012562101.01.01.01.09.01000005.01.000048e+09MN25Battery Park City-Lower Manhattan
601ALTERATION TYPE 11000005MANHATTAN1NEW YORK PLAZA4.07501.010004.009/13/2021 12:00:00 AMCO Issued8582Final101.010/15/21 11:03:48 AMCO-000008582980767.0195231.040.702540-74.012562101.01.01.01.09.01000005.01.000048e+09MN25Battery Park City-Lower Manhattan
701ALTERATION TYPE 11000037MANHATTAN74BROAD STREET11.017.010004.008/20/2021 12:00:00 AMCO Issued7521Renewal Without Change101.009/29/21 8:48:28 AMCO-000007521981042.0196000.040.704651-74.011570101.01.01.01.09.01000037.01.000110e+09MN25Battery Park City-Lower Manhattan
801ALTERATION TYPE 11000037MANHATTAN74BROAD STREET11.017.010004.009/30/2021 12:00:00 AMCO Issued9343Final101.001/25/22 4:10:34 PMCO-000009343981042.0196000.040.704651-74.011570101.01.01.01.09.01000037.01.000110e+09MN25Battery Park City-Lower Manhattan
901ALTERATION TYPE 11000045MANHATTAN25BROADWAY13.027.010004.003/01/2022 12:00:00 AMCO Issued15797Renewal Without Change101.003/01/22 2:57:15 PMCO-000015797980542.0196401.040.705752-74.013374101.01.01.01.013.01000045.01.000130e+09MN25Battery Park City-Lower Manhattan

Last rows

JOB FILING NAMEJOB TYPEBINBOROUGHHOUSE NOSTREET NAMEBLOCKLOTZIP CODESUBMITTED DATEC OF O STATUSC OF O SEQUENCE #C OF O FILING TYPECOMMUNITY BOARDC OF O ISSUANCE DATEAPPLICATION NUMBERxCoordinateyCoordinatelatitudelongitudecommunityDistrictcommunityDistrictBoroughCodecommunityDistrictNumbercityCouncilDistrictcensusTract2010buildingIdentificationNumberbblntantaName
14256I1New Building5175076STATEN ISLAND173GARSOMMER AVENUE2223.013.010314.011/01/2021 12:00:00 AMCO Issued10771Final502.011/12/21 12:34:07 PMCO-000010771937368.0160894.040.608169-74.168845502.05.02.050.029103.0NaNNaNSI05New Springville-Bloomfield-Travis
14257I1New Building5175080STATEN ISLAND38ATLANTIC AVENUE3293.028.010304.004/11/2022 12:00:00 AMCO Issued18093Final502.004/25/22 2:48:45 PMCO-000018093957318.0155843.040.594389-74.096975502.05.02.050.09602.0NaNNaNSI36Old Town-Dongan Hills-South Beach
14258I1New Building5175081STATEN ISLAND38GARATLANTIC AVENUE3293.028.010304.004/11/2022 12:00:00 AMCO Issued18094Final502.004/25/22 1:24:20 PMCO-000018094957318.0155843.040.594389-74.096975502.05.02.050.09602.0NaNNaNSI36Old Town-Dongan Hills-South Beach
14259I1New Building5175083STATEN ISLAND42GARATLANTIC AVENUE3293.030.010304.004/12/2022 12:00:00 AMCO Issued18097Final502.004/25/22 2:49:46 PMCO-000018097957340.0155827.040.594345-74.096896502.05.02.050.09602.0NaNNaNSI36Old Town-Dongan Hills-South Beach
14260I1New Building5863165STATEN ISLAND1EVENTS PLAZA9999.01.010301.002/02/2022 12:00:00 AMCO Issued14949Renewal Without Change501.002/02/22 9:42:56 PMCO-000014949NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
14261I1New Building5863165STATEN ISLAND1EVENTS PLAZA9999.01.010301.002/11/2022 12:00:00 AMCO Issued15415Renewal Without Change501.002/11/22 7:02:48 PMCO-000015415NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
14262I1New Building5863165STATEN ISLAND1EVENTS PLAZA9999.01.010301.002/25/2022 12:00:00 AMCO Issued16063Renewal With Change501.002/25/22 9:21:51 PMCO-000016063NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
14263I1New Building5863165STATEN ISLAND1EVENTS PLAZA9999.01.010301.008/06/2021 12:00:00 AMCO Issued6833Renewal With Change501.008/06/21 9:40:02 PMCO-000006833NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
14264I1New Building5863165STATEN ISLAND1EVENTS PLAZA9999.01.010301.012/10/2021 12:00:00 AMCO Issued12547Renewal With Change501.012/10/21 9:05:21 PMCO-000012547NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
14265I1New Building5863165STATEN ISLAND1EVENTS PLAZA9999.01.010301.012/14/2021 12:00:00 AMCO Issued12617Renewal With Change501.012/14/21 5:42:06 PMCO-000012617NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN